Intelligent document processing
An OCR/IDP pipeline that reads, understands and routes millions of shipping documents at near-perfect accuracy.
The brief
A logistics operation drowned in paper — bills of lading, customs forms, invoices, proofs of delivery — arriving as scans and phone photos in countless layouts. Teams keyed them by hand, which was slow, error-prone, and impossible to scale to the millions of documents flowing through the network.
They needed a pipeline that could read almost anything, extract and validate the fields that mattered, route each document correctly, and escalate only genuine exceptions — at a scale and accuracy people simply couldn't match.
- Read and route millions of shipping documents automatically.
- Handle scans, photos and many layouts — including handwriting.
- Reach near-perfect field accuracy on validated documents.
- Validate against business and customs rules; escalate only uncertain cases.
- Maintain a verifiable audit trail at document scale.
- Scale to peak volumes without adding headcount.
Our AI-native approach
We built an IDP/ICR pipeline that treats each document as a sequence of scored decisions — classify, extract, validate, route — each carrying a confidence value. High-confidence documents pass straight through; anything uncertain goes to a focused human review with the model's best guess pre-filled.
We began by mining the real document corpus to understand the true distribution of layouts, languages and edge cases before writing a line of extraction logic.
What we built
Any-format intake
Scans, photos and digital files are normalised, de-skewed and quality-scored on entry.
Classification & routing
Each document is identified by type and sent to the right workflow.
OCR & ICR extraction
Printed and handwritten fields are read, with confidence on every value.
Rule validation
Extracted data is checked against business and customs rules automatically.
Exception handling
Only low-confidence documents reach a human, pre-filled with the model's proposal.
Audit trail at scale
Every field carries its source and confidence across millions of documents.
How we built it
Discovery started with the corpus itself, so the pipeline was designed for reality rather than an idealised sample. We made confidence a first-class concept early, because it is what lets the system route work safely between automation and human review.
We delivered by document type in phases, each gated on accuracy against a human-labelled set before going live. Reviewer corrections fed back into training from day one, so accuracy compounded as volume grew.
How it works
Ingest
Documents are normalised and quality-scored.
Classify
Type and layout are identified.
Extract
OCR/NLP pulls structured fields with confidence.
Validate
Rules and cross-checks confirm or flag each value.
Route
Clean documents pass through; uncertain ones go to review.
The extraction stack pairs OCR with NLP classification and named-entity models tuned to the document set. Confidence is first-class: it decides routing, so the system gets faster as the models learn, and reviewers spend their time only on genuine ambiguity.
Validated corrections feed back into training, so at twelve million documents accuracy compounds rather than plateaus.
The impact
Manual document handling fell dramatically while throughput rose.
Field accuracy held near-perfect across millions of documents.
Peak volumes were absorbed without temporary staffing.
Reviewers shifted from data entry to genuine exception handling.
Technology stack
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